Abstract
In many biomedical studies, participants are monitored at periodic visits until the occurrence of the failure event. Biomarkers are often measured repeatedly during these visits, and such measurements can facilitate updated disease prediction. In this work, we propose a two-dimensional incident dynamic area under curve (AUC), to capture the variability due to both the biomarker assessment time and the prediction time to comprehensively quantify the predictive performance of a longitudinal biomarker. We propose a pseudo partial-likelihood to achieve consistent estimation of the AUC under two realistic scenarios of visit schedules. Variance estimation methods are designed to facilitate inferential procedures. We examine the finite-sample performance of our method through extensive simulations. The methods are applied to a study of chronic myeloid leukemia to evaluate the predictive performance of longitudinally collected gene expression levels.
Original language | English (US) |
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Pages (from-to) | 5065-5077 |
Number of pages | 13 |
Journal | Statistics in Medicine |
Volume | 40 |
Issue number | 23 |
DOIs | |
State | Published - Oct 15 2021 |
Keywords
- area under curve
- longitudinal biomarker
- predictive discrimination
- pseudo partial-likelihoods
- survival outcome
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
MD Anderson CCSG core facilities
- Biostatistics Resource Group